Small RNAseq: Differential Expression Analysis

Environment Setup

salloc -N 1 --exclusive -p amd -t 8:00:00
conda env create -f conda-env.yml
conda activate smallrna

Downloading datasets

Raw data

Raw data was downloaded from the sequencing facility using the secure link, with wget command. The downloaded files were checked for md5sum and compared against list of files expected as per the input samples provided.

wget https://oc1.rnet.missouri.edu/xyxz
# link masked 
# GEO link will be included later
# merge files of same samples (technical replicates)
paste <(ls *_L001_R1_001.fastq.gz) <(ls *_L002_R1_001.fastq.gz) | \
   sed 's/\t/ /g' |\
   awk '{print "cat",$1,$2" > "$1}' |\
   sed 's/_L001_R1_001.fastq.gz/.fq.gz/2' > concatenate.sh
chmod +x concatenate.sh
sh concatenate.sh

Genome/annotation

Additional files required for the analyses were downloaded from GenCode. The downloaded files are as follows:

wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/GRCm39.primary_assembly.genome.fa.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/gencode.vM30.annotation.gff3.gz
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M30/gencode.vM30.annotation.gtf.gz
gunzip GRCm39.primary_assembly.genome.fa.gz
gunzip gencode.vM30.annotation.gff3.gz
gunzip gencode.vM30.annotation.gtf.gz

FastQC (before processing)

for fq in *.fq.gz; do
  fastqc --threads $SLURM_JOB_CPUS_PER_NODE $fq;
done
mkdir -p fastqc_pre
mv *.zip *.html fastqc_pre/

Mapping

To index the genome, following command was run (in an interactive session).

fastaGenome="GRCm39.genome.fa"
gtf="gencode.vM30.annotation.gtf"
STAR --runThreadN $SLURM_JOB_CPUS_PER_NODE \
     --runMode genomeGenerate \
     --genomeDir $(pwd) \
     --genomeFastaFiles $fastaGenome \
     --sjdbGTFfile $gtf \
     --sjdbOverhang 1

Each fastq file was mapped to the indexed genome as using runSTAR_map.sh script shown below:

#!/bin/bash
read1=$1
out=$(basename ${read1%%.*})
STARgenomeDir=$(pwd)
# illumina adapter
adapterseq="AGATCGGAAGAGC"
STAR \
    --genomeDir ${STARgenomeDir} \
    --readFilesIn ${read1} \
    --outSAMunmapped Within \
    --readFilesCommand zcat \
    --outSAMtype BAM SortedByCoordinate \
    --quantMode GeneCounts \
    --outFilterMultimapNmax 20 \
    --clip3pAdapterSeq ${adapterseq} \
    --clip3pAdapterMMp 0.1 \
    --outFilterMismatchNoverLmax 0.03 \
    --outFilterScoreMinOverLread 0 \
    --outFilterMatchNminOverLread 0 \
    --outFilterMatchNmin 16 \
    --outFileNamePrefix ${out} \
    --alignSJDBoverhangMin 1000 \ 
    --alignIntronMax 1 \
    --runThreadN ${SLURM_JOB_CPUS_PER_NODE} \
    --genomeLoad LoadAndKeep \
    --limitBAMsortRAM 30000000000 \
    --outSAMheaderHD "@HD VN:1.4 SO:coordinate"

Mapping was run with a simple loop:

for fq in *.fq.gz; do
  runSTAR_map.sh $fq;
done

Counting Stats

Counts generated by STAR with option --quantMode GeneCounts were parsed to generate summary stats as well as to extract annotated small RNA feature counts.

mkdir -p counts_files
# copy counts for each sample
cp *ReadsPerGene.out.tab counts_files/
cd counts_files
# merge counts
join_files.sh *ReadsPerGene.out.tab |\
   sed 's/ReadsPerGene.out.tab//g' |\
   grep -v "^N_" > counts_star.tsv
# merge stats
join_files.sh *ReadsPerGene.out.tab |\
   sed 's/ReadsPerGene.out.tab//g' |\
   head -n 1 > summary_star.tsv
join_files.sh *ReadsPerGene.out.tab |\
   sed 's/ReadsPerGene.out.tab//g' |\
   grep "^N_" >> summary_star.tsv
# parse GTF to extact gene.id and its biotype:
gtf=gencode.vM30.annotation.gtf
awk 'BEGIN{OFS=FS="\t"} $3=="gene" {split($9,a,";"); print a[1],a[2]}' ${gtf} |\
   awk '{print $4"\t"$2}' |\
   sed 's/"//g' > GeneType_GeneID.tsv
cut -f 1 GeneType_GeneID.tsv | sort |uniq > features.txt

The information for biotype as provided by the gencodegenes were used for categorizing biotype.

The smallRNA group consists of following biotype:

miRNA
misc_RNA
scRNA
snRNA
snoRNA
sRNA
scaRNA

The full table is as follows:

library(knitr)
setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
file1="assets/GeneType_Group.tsv"
info <-
  read.csv(
    file1,
    header = TRUE,
    sep = "\t",
    stringsAsFactors = TRUE
  )
kable(info, caption = "Table 1: biotype and its groupings")
Table 1: biotype and its groupings
biotype group
protein_coding coding_genes
pseudogene pseudogenes
TR_C_gene Ig_genes
TR_D_gene Ig_genes
TR_J_gene Ig_genes
TR_V_gene Ig_genes
IG_C_gene Ig_genes
IG_D_gene Ig_genes
IG_J_gene Ig_genes
IG_LV_gene Ig_genes
IG_V_gene Ig_genes
TR_J_pseudogene pseudogenes
TR_V_pseudogene pseudogenes
IG_C_pseudogene pseudogenes
IG_D_pseudogene pseudogenes
IG_pseudogene pseudogenes
IG_V_pseudogene pseudogenes
lncRNA long_non_conding_RNA
miRNA non_conding_RNA
misc_RNA non_conding_RNA
ribozyme non_conding_RNA
rRNA non_conding_RNA
scaRNA non_conding_RNA
scRNA non_conding_RNA
snoRNA non_conding_RNA
snRNA non_conding_RNA
sRNA non_conding_RNA
Mt_rRNA non_conding_RNA
Mt_tRNA non_conding_RNA
processed_pseudogene pseudogenes
unprocessed_pseudogene pseudogenes
translated_unprocessed_pseudogene pseudogenes
transcribed_processed_pseudogene pseudogenes
transcribed_unitary_pseudogene pseudogenes
transcribed_unprocessed_pseudogene pseudogenes
unitary_pseudogene pseudogenes
TEC unconfirmed_genes

A samples table (samples.tsv) categorizing samples to its condition were also generated:

file2="assets/samples.tsv"
samples <-
  read.csv(
    file2,
    header = TRUE,
    sep = "\t",
    stringsAsFactors = TRUE
  )
kable(samples, caption = "Table 2: Samples in the study")
Table 2: Samples in the study
Sample Group
Dif_D6_1_S4 Diff
Dif_D6_2_S3 Diff
Dif_D6_3_S2 Diff
Dif_D6_4_S1 Diff
Undif_D2_1_S8 Undf
Undif_D2_2_S7 Undf
Undif_D2_3_S6 Undf
Undif_D2_4_S5 Undf

This information was then merged withe counts table to generate QC plots:

awk 'BEGIN{OFS=FS="\t"}FNR==NR{a[$1]=$2;next}{ print $2,$1,a[$1]}' \
    GeneType_Group.tsv GeneType_GeneID.tsv > GeneID_GeneType_Group.tsv

awk 'BEGIN{OFS=FS="\t"}FNR==NR{a[$1]=$2"\t"$3;next}{print $1,a[$1],$0}' \
    GeneID_GeneType_Group.tsv counts_star.tsv |\
    cut -f 1-3,5- > processed_counts_star.tsv

Plotting the mapping summary and count statistics for various biotypes:

library(scales)
library(tidyverse)
library(plotly)
setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
file1="assets/processed_counts_star.tsv"
file2="assets/summary_stats_star.tsv"
counts <-
  read.csv(
    file1,
    sep = "\t",
    stringsAsFactors = TRUE
  )
subread <-
  read.csv(
    file2,
    sep = "\t",
    stringsAsFactors = TRUE
  )
# convert long format
counts.long <- gather(counts, Sample, Count, Dif_D6_1_S4:Undif_D2_4_S5, factor_key=TRUE)
subread.long <- gather(subread, Sample,  Count, Dif_D6_1_S4:Undif_D2_4_S5, factor_key=TRUE)
# organize
counts.long$Group <-
  factor(
    counts.long$Group,
    levels = c(
      "coding_genes",
      "non_conding_RNA",
      "long_non_conding_RNA",
      "pseudogenes",
      "unconfirmed_genes",
      "Ig_genes"
    )
  )

subread.long$Assignments <-
  factor(
    subread.long$Assignments,
    levels = c(
      "N_input",
      "N_unmapped",
      "N_multimapping",
      "N_unique",
      "N_ambiguous",
      "N_noFeature"
    )
  )
ggplot(subread.long, aes(x = Assignments, y = Count, fill = Assignments)) +
  geom_bar(stat = 'identity') +
  labs(x = "Subread assingments", y = "reads") + theme_minimal() +
  scale_y_continuous(labels = label_comma()) +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1,
      size = 12
      ),
    strip.text = element_text(
      face = "bold",
      color = "gray35",
      hjust = 0,
      size = 10
    ),
    strip.background = element_rect(fill = "white", linetype = "blank"),
    legend.position = "none"
  ) +
  facet_wrap("Sample", scales = "free_y", ncol = 4)
Figure 1: STAR read mapping and feature assignment. Here, `N_input` is total input reads, `N_unmapped` is reads that were either too short to map after adapter removal or had higher mismatch rate to place reliably on the genome, `N_multimapping` is reads mapped to multiple loci, `N_unique` is reads mapped to unique loci. A subset of `N_unique` reads that were unable to clearly assign to a feature or assign any feature at all are grouped as `N_ambigious` or `N_noFeature`, respectively

Figure 1: STAR read mapping and feature assignment. Here, N_input is total input reads, N_unmapped is reads that were either too short to map after adapter removal or had higher mismatch rate to place reliably on the genome, N_multimapping is reads mapped to multiple loci, N_unique is reads mapped to unique loci. A subset of N_unique reads that were unable to clearly assign to a feature or assign any feature at all are grouped as N_ambigious or N_noFeature, respectively 

g <- ggplot(counts.long, aes(x = Group, y = Count, fill = Group)) +
  geom_bar(stat = 'sum') +
  labs(x = "biotype", y = "read counts") + theme_minimal() +
  scale_y_continuous(labels = label_comma()) +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1,
      size = 12
    ),
    strip.text = element_text(
      face = "bold",
      color = "gray35",
      hjust = 0,
      size = 10
    ),
    strip.background = element_rect(fill = "white", linetype = "blank"),
    legend.position = "none"
  ) +
  facet_wrap("Sample", scales = "free_y", ncol = 4)
#ggplotly(g)
g
Figure 2: Features with read counts

Figure 2: Features with read counts 

counts.nc <- filter(counts.long, Group %in% "non_conding_RNA" )
counts.nc$GeneType <-
  factor(
    counts.nc$GeneType,
    levels = c(
      "miRNA",
      "misc_RNA",
      "snoRNA",
      "snRNA",
      "sRNA",
      "scRNA",
      "scaRNA",
      "Mt_tRNA",
      "Mt_rRNA",
      "rRNA",
      "ribozyme"
    )
  )

g <- ggplot(counts.nc, aes(x = GeneType, y = Count, fill = GeneType)) +
  geom_bar(stat = 'sum') +
  labs(x = "biotype", y = "read counts") + theme_minimal() +
  scale_y_continuous(labels = label_comma()) +
  theme(
    axis.text.x = element_text(
      angle = 45,
      vjust = 1,
      hjust = 1,
      size = 12
    ),
    strip.text = element_text(
      face = "bold",
      color = "gray35",
      hjust = 0,
      size = 10
    ),
    strip.background = element_rect(fill = "white", linetype = "blank"),
    legend.position = "none"
  ) +
  facet_wrap("Sample", scales = "free_y", ncol = 4)
#ggplotly(g)
g
Figure 3: non-coding biotype read counts

Figure 3: non-coding biotype read counts

subset the counts file to select only smallRNA genes

snrna <- c('miRNA',
           'misc_RNA',
           'scRNA',
           'snRNA',
           'snoRNA',
           'sRNA',
           'scaRNA')
cts <- filter(counts, GeneType %in% snrna) %>%
  select(Geneid, Dif_D6_1_S4:Undif_D2_4_S5)
write_delim(cts, file = "assets/noncoding_counts_star.tsv", delim = "\t")

This noncoding_counts_star.tsv and samples.tsv file will be used for DESeq2 analyses.

DESeq2

For the next steps, we used DESeq2 for performing the DE analyses. Results were visualized as volcano plots and tables were exported to excel.

Load packages

setwd("/work/LAS/geetu-lab/arnstrm/mouse.trophoblast.smallRNAseq")
library(DESeq2)
library(RColorBrewer)
library(pheatmap)
library(genefilter)
library(ggrepel)
library(biomaRt)
library(reshape2)
library(PupillometryR)
library(ComplexUpset)

Import counts and sample metadata

The counts data and its associated metadata (coldata) are imported for analyses.

counts = 'assets/noncoding_counts_star.tsv'
groupFile = 'assets/samples.tsv'
coldata <-
  read.csv(
    groupFile,
    row.names = 1,
    sep = "\t",
    stringsAsFactors = TRUE
  )
cts <- as.matrix(read.csv(counts, sep = "\t", row.names = "Geneid"))

Reorder columns of cts according to coldata rows. Check if samples in both files match.

colnames(cts)
#> [1] "Dif_D6_1_S4"   "Dif_D6_2_S3"   "Dif_D6_3_S2"   "Dif_D6_4_S1"  
#> [5] "Undif_D2_1_S8" "Undif_D2_2_S7" "Undif_D2_3_S6" "Undif_D2_4_S5"
all(rownames(coldata) %in% colnames(cts))
#> [1] TRUE
cts <- cts[, rownames(coldata)]

Normalize

The batch corrected read counts are then used for running DESeq2 analyses

dds <- DESeqDataSetFromMatrix(countData = cts,
                              colData = coldata,
                              design = ~ Group)
vsd <- vst(dds, blind = FALSE, nsub =500)
keep <- rowSums(counts(dds)) >= 5
dds <- dds[keep, ]
dds <- DESeq(dds)
dds
#> class: DESeqDataSet 
#> dim: 1266 8 
#> metadata(1): version
#> assays(4): counts mu H cooks
#> rownames(1266): ENSMUSG00000119106.1 ENSMUSG00000119589.1 ...
#>   ENSMUSG00000065444.3 ENSMUSG00000077869.3
#> rowData names(22): baseMean baseVar ... deviance maxCooks
#> colnames(8): Dif_D6_1_S4 Dif_D6_2_S3 ... Undif_D2_3_S6 Undif_D2_4_S5
#> colData names(2): Group sizeFactor
vst <- assay(vst(dds, blind = FALSE, nsub = 500))
vsd <- vst(dds, blind = FALSE, nsub = 500)
pcaData <-
  plotPCA(vsd,
          intgroup = "Group",
          returnData = TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))

PCA plot for QC

PCA plot for the dataset that includes all libraries.

rv <- rowVars(assay(vsd))
select <-
  order(rv, decreasing = TRUE)[seq_len(min(500, length(rv)))]
pca <- prcomp(t(assay(vsd)[select, ]))
percentVar <- pca$sdev ^ 2 / sum(pca$sdev ^ 2)
intgroup = "Group"
intgroup.df <- as.data.frame(colData(vsd)[, intgroup, drop = FALSE])
group <- if (length(intgroup) == 1) {
  factor(apply(intgroup.df, 1, paste, collapse = " : "))
}
d <- data.frame(
  PC1 = pca$x[, 1],
  PC2 = pca$x[, 2],
  intgroup.df,
  name = colnames(vsd)
)

plot PCA for components 1 and 2

#nudge <- position_nudge(y = 0.5)
g <- ggplot(d, aes(PC1, PC2, color = Group)) +
  scale_shape_manual(values = 1:8) +
  theme_bw() +
  theme(legend.title = element_blank()) +
  geom_point(size = 2, stroke = 2) +
  geom_text_repel(aes(label = name)) +
  xlab(paste("PC1", round(percentVar[1] * 100, 2), "% variance")) +
  ylab(paste("PC2", round(percentVar[2] * 100, 2), "% variance"))
g
Figure 4: PCA plot for the first 2 principal components

Figure 4: PCA plot for the first 2 principal components 

#g

Sample distance for QC

sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix( sampleDists )
rownames(sampleDistMatrix) <- colnames(vsd)
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists,
         col = colors)
Figure 5: Euclidean distance between samples

Figure 5: Euclidean distance between samples

Set contrasts and find DE genes

resultsNames(dds)
#> [1] "Intercept"          "Group_Undf_vs_Diff"
res.UndfvsDiff <- results(dds, contrast = c("Group", "Undf", "Diff"))
table(res.UndfvsDiff$padj < 0.05)
#> 
#> FALSE  TRUE 
#>   579   294
res.UndfvsDiff <- res.UndfvsDiff[order(res.UndfvsDiff$padj),]
res.UndfvsDiffdata <-
  merge(
    as.data.frame(res.UndfvsDiff),
    as.data.frame(counts(dds, normalized = TRUE)),
    by = "row.names",
    sort = FALSE
  )

Creating gene lists

The gene lists have Ensembl gene-ID-version. We need mirbase_id, gene_biotype and external_gene_name attached to make the results interpretable. So we will download metadata from ensembl using biomaRt package.

ensembl = useMart("ENSEMBL_MART_ENSEMBL")
ensembl = useDataset("mmusculus_gene_ensembl", mart = ensembl)
filterType <- "ensembl_gene_id_version"
filterValues <- rownames(cts)
attributeNames <- c('ensembl_gene_id_version',
                    'gene_biotype',
                    'mirbase_id',
                    'external_gene_name')
annot <- getBM(
  attributes = attributeNames,
  filters = filterType,
  values = filterValues,
  mart = ensembl
)
isDup <- duplicated(annot$ensembl_gene_id)
dup <- annot$ensembl_gene_id[isDup]
annot <- annot[!annot$ensembl_gene_id %in% dup, ] #this object will be saved and used later

Write result tables

Here, we will attach the mirbase_id, gene_biotype and external_gene_name downloaded in the previous section to the results table. We will also filter the the table to write:

  1. DE list that have padj less than or equal to 0.05
  2. DE list that have padj less than or equal to 0.05, and log2FoldChange greater than or equal to fold change of 1.5
  3. DE list that have padj less than or equal to 0.05, and log2FoldChange less than or equal to fold change of -1.5
  4. Full list of DE table without any filtering.
names(res.UndfvsDiffdata)[1] <- "ensembl_gene_id_version"
res.UndfvsDiffdata <-
  merge(x = res.UndfvsDiffdata,
        y = annot,
        by = "ensembl_gene_id_version",
        all.x = TRUE)
res.UndfvsDiffdata <-
  res.UndfvsDiffdata[, c(1, (ncol(res.UndfvsDiffdata) - 2):ncol(res.UndfvsDiffdata), 2:(ncol(res.UndfvsDiffdata) -
                                                                                          3))]
res.UndfvsDiffSig <- res.UndfvsDiffdata %>%
  filter(padj <= 0.05)
fc = 1.5
log2fc = log(fc, base = 2)
neg.log2fc = log2fc * -1
res.UndfvsDiffSig.up <- res.UndfvsDiffdata %>%
  filter(padj <= 0.05 & log2FoldChange >= log2fc)
res.UndfvsDiffSig.dw <- res.UndfvsDiffdata %>%
  filter(padj <= 0.05 & log2FoldChange <= neg.log2fc)

write_delim(res.UndfvsDiffdata, file = "DESeq2_results_UndfvsDiff_full-table.tsv", delim = "\t")
write_delim(res.UndfvsDiffSig, file = "DESeq2_results_UndfvsDiff_adj.p-le-0.05.tsv", delim = "\t")
write_delim(res.UndfvsDiffSig.up, file = "DESeq2_results_UndfvsDiff_adj.p-le-0.05_fc-ge-1.5.tsv", delim = "\t")
write_delim(res.UndfvsDiffSig.dw, file = "DESeq2_results_UndfvsDiff_adj.p-le-0.05_fc-le-neg1.5.tsv", delim = "\t")

Highly expressed genes for conditions

exp <- res.UndfvsDiffdata[, c(1:4, 11:ncol(res.UndfvsDiffdata))]
exp$external_gene_name <-
  ifelse(exp$external_gene_name == "",
         exp$ensembl_gene_id_version,
         exp$external_gene_name)
exp$gene <- paste0(exp$external_gene_name, "(", exp$gene_biotype, ")")
renamed.exp <- exp[, c(13, 1:12)]
renamed.exp.long <-
  melt(
    renamed.exp,
    id.vars = c(
      'gene',
      'ensembl_gene_id_version',
      'gene_biotype',
      'mirbase_id',
      'external_gene_name'
    )
  )
colnames(renamed.exp.long) <-
  c('gene',
    'ensembl_gene_id_version',
    'gene_biotype',
    'mirbase_id',
    'external_gene_name',
    'replicates',
    'norm.expression'
  )
renamed.exp.long$condition <- "NA"
renamed.exp.long$condition[which(str_detect(renamed.exp.long$replicates, "Dif_D6"))] <-
  "Diff"
renamed.exp.long$condition[which(str_detect(renamed.exp.long$replicates, "Undif_D2"))] <-
  "Undiff"
renamed.exp.long <-
  renamed.exp.long  %>% filter(norm.expression > 0)
renamed.exp.long <- na.omit(renamed.exp.long)
# SOURCE: https://ourcodingclub.github.io/tutorials/dataviz-beautification/
theme_niwot <- function() {
  theme_bw() +
    theme(
      axis.text = element_text(size = 10),
      axis.title = element_text(size = 12),
      axis.line.x = element_line(color = "black"),
      axis.line.y = element_line(color = "black"),
      panel.border = element_blank(),
      panel.grid.major.x = element_blank(),
      panel.grid.minor.x = element_blank(),
      panel.grid.minor.y = element_blank(),
      panel.grid.major.y = element_blank(),
      plot.margin = unit(c(1, 1, 1, 1), units = , "cm"),
      plot.title = element_text(
        size = 8,
        vjust = 1,
        hjust = 0
      ),
      legend.text = element_text(size = 8),
      legend.title = element_blank(),
      legend.position = c(0.95, 0.15),
      legend.key = element_blank(),
      legend.background = element_rect(
        color = "black",
        fill = "transparent",
        size = 2,
        linetype = "blank"
      )
    )
}

Normalized expression plots

A <- ggplot(data = renamed.exp.long,
       aes(x = replicates, y = norm.expression, fill = condition)) +
  geom_flat_violin(position = position_nudge(x = 0.2, y = 0),
                   alpha = 0.8,
                   trim = FALSE) +
  geom_point(
    aes(y = norm.expression, color = condition),
    position = position_jitter(width = 0.15),
    size = 1,
    alpha = 0.5
  ) +
  geom_boxplot(width = 0.2,
               outlier.shape = NA,
               alpha = 0.8) + stat_summary(
                 fun = mean,
                 geom = "point",
                 shape = 23,
                 size = 2
               ) +
  labs(y = "\nNormalized Expression", x = NULL) +
  guides(fill = "none", color = "none") +
  scale_y_log10(label = comma)  +
  theme_niwot() + theme(axis.text.x = element_text(
    angle = 45,
    vjust = 1,
    hjust = 1
  ))
B <- ggplot(data = renamed.exp.long,
       aes(x = condition, y = norm.expression, fill = condition)) +
  geom_flat_violin(position = position_nudge(x = 0.2, y = 0),
                   alpha = 0.8,
                   trim = FALSE) +
  geom_point(
    aes(y = norm.expression, color = condition),
    position = position_jitter(width = 0.15),
    size = 1,
    alpha = 0.5
  ) +
  geom_boxplot(width = 0.2,
               outlier.shape = NA,
               alpha = 0.8) + stat_summary(
                 fun = mean,
                 geom = "point",
                 shape = 23,
                 size = 2
               ) +
  labs(y = "\nNormalized Expression", x = NULL) +
  guides(fill = "none", color = "none") +
  scale_y_log10(label = comma)  +
  theme_niwot() + theme(axis.text.x = element_text(
    angle = 45,
    vjust = 1,
    hjust = 1
  ))
C <- ggplot(data = renamed.exp.long,
       aes(x = gene_biotype, y = norm.expression, fill = condition)) +
  geom_flat_violin(position = position_nudge(x = 0.2, y = 0),
                   alpha = 0.8,
                   trim = FALSE) +
  geom_point(
    aes(y = norm.expression, color = condition),
    position = position_jitter(width = 0.15),
    size = 1,
    alpha = 0.5
  ) +
  geom_boxplot(width = 0.2,
               outlier.shape = NA,
               alpha = 0.8) + stat_summary(
                 fun = mean,
                 geom = "point",
                 shape = 23,
                 size = 2
               ) +
  labs(y = "\nNormalized Expression", x = NULL) +
  guides(fill = "none", color = "none") +
  scale_y_log10(label = comma)  +
  theme_niwot() + theme(axis.text.x = element_text(
    angle = 45,
    vjust = 1,
    hjust = 1
  ))

Expression plots

Replicates

A
Figure 6A: Normalized expression of genes in undifferentiated and differentiated samples (replicate)

Figure 6A: Normalized expression of genes in undifferentiated and differentiated samples (replicate)

Conditions

B
Figure 6B: Normalized expression of genes in undifferentiated and differentiated samples (condition)

Figure 6B: Normalized expression of genes in undifferentiated and differentiated samples (condition)

Biotypes

C
Figure 6C: Normalized expression of genes in undifferentiated and differentiated samples (split based on gene biotype)

Figure 6C: Normalized expression of genes in undifferentiated and differentiated samples (split based on gene biotype)

Highly expressed small RNAs

renamed.exp.means <- 
  renamed.exp %>% rowwise() %>% mutate(Diff = mean(c(
  Dif_D6_1_S4, Dif_D6_2_S3, Dif_D6_3_S2, Dif_D6_4_S1
)))
renamed.exp.means <-
  renamed.exp.means %>% rowwise() %>% mutate(Undiff = mean(c(
    Undif_D2_1_S8, Undif_D2_2_S7, Undif_D2_3_S6, Undif_D2_4_S5
  )))
df.diff <-
  renamed.exp.means %>%
  dplyr::select(gene:external_gene_name, Diff) %>% 
  dplyr::filter(Diff > 0)
df.undiff <-
  renamed.exp.means %>%
  dplyr::select(gene:external_gene_name, Undiff) %>%
  dplyr::filter(Undiff > 0)

df.diff$quart <- ntile(df.diff$Diff, 4)
df.diff$decile <- ntile(df.diff$Diff, 10)
df.undiff$quart <- ntile(df.undiff$Undiff, 4)
df.undiff$decile <- ntile(df.undiff$Undiff, 10)
undiff.75pc <-
  df.undiff %>%
  dplyr::filter(quart == 4) %>%
  dplyr::select(ensembl_gene_id_version)
undiff.90pc <-
  df.undiff %>%
  dplyr::filter(decile == 10) %>%
  dplyr::select(ensembl_gene_id_version)
diff.75pc <-
  df.diff %>%
  dplyr::filter(quart == 4) %>%
  dplyr::select(ensembl_gene_id_version)
diff.90pc <-
  df.diff %>%
  dplyr::filter(decile == 10) %>%
  dplyr::select(ensembl_gene_id_version)

data.A <- diff.90pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.A$diff.90pc = 1
data.B <- undiff.90pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.B$undiff.90pc = 1
decile.data <- merge(data.A, data.B, by=0, all.x=TRUE, all.y=TRUE)
decile.data[is.na(decile.data)] <- 0
decile.data <-
  merge(x = decile.data,
        y = annot,
        by.x = "Row.names",
        by.y = "ensembl_gene_id_version",
        all.x = TRUE)
decile.data <- decile.data %>% dplyr::select(Row.names:gene_biotype)
decile.data <- decile.data %>% column_to_rownames(var="Row.names")

data.A <- diff.75pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.A$diff.75pc = 1
data.B <- undiff.75pc %>% column_to_rownames(var="ensembl_gene_id_version")
data.B$undiff.75pc = 1
quartile.data <- merge(data.A, data.B, by=0, all.x=TRUE, all.y=TRUE)
quartile.data[is.na(quartile.data)] <- 0
quartile.data <-
  merge(x = quartile.data,
        y = annot,
        by.x = "Row.names",
        by.y = "ensembl_gene_id_version",
        all.x = TRUE)
quartile.data <- quartile.data %>% dplyr::select(Row.names:gene_biotype)
quartile.data <- quartile.data %>% column_to_rownames(var="Row.names")
inter <- colnames(decile.data)[1:2]
decile.plot <- upset(
  decile.data,
  inter,
  annotations = list(
    'gene_biotype' = (
      ggplot(mapping = aes(fill = gene_biotype))
      + geom_bar(stat = 'count', position = 'fill')
      + scale_y_continuous(labels = scales::percent_format())
      + scale_fill_manual(
        values = c(
          'miRNA' = '#8142c8',
          'misc_RNA' = '#f62189',
          'snRNA' = '#008cd4',
          'snoRNA' = '#7c4515',
          'scaRNA' = '#e9a3ff',
          'sRNA' = '#e7c08b'
        )
      )
      + ylab('smallRNA proportion')
    )
  ),
      queries=list(
        upset_query(
            intersect=c('diff.90pc', 'undiff.90pc'),
            color='orange',
            fill='orange',
            only_components=c('intersections_matrix', 'Intersection size')
        ),
        upset_query(
            intersect='diff.90pc',
            color='tomato',
            fill='tomato',
            only_components=c('intersections_matrix', 'Intersection size')
        ),
        upset_query(
            intersect='undiff.90pc',
            color='lightseagreen',
            fill='lightseagreen',
            only_components=c('intersections_matrix', 'Intersection size')
        )
    ),
  width_ratio = 0.3,
  set_sizes = (
    upset_set_size(geom = geom_bar(aes(
      fill = gene_biotype, x = group
    ),
    width = 0.8),
    position = 'right') + theme(legend.position = "none") +
      scale_fill_manual(
        values = c(
          'miRNA' = '#8142c8',
          'misc_RNA' = '#f62189',
          'snRNA' = '#008cd4',
          'snoRNA' = '#7c4515',
          'scaRNA' = '#e9a3ff',
          'sRNA' = '#e7c08b'
        )
      )
  ),
  guides = 'over'
)
inter <- colnames(quartile.data)[1:2]
quartile.plot <- upset(
  quartile.data,
  inter,
  annotations = list(
    'gene_biotype' = (
      ggplot(mapping = aes(fill = gene_biotype))
      + geom_bar(stat = 'count', position = 'fill')
      + scale_y_continuous(labels = scales::percent_format())
      + scale_fill_manual(
        values = c(
          'miRNA' = '#8142c8',
          'misc_RNA' = '#f62189',
          'snRNA' = '#008cd4',
          'snoRNA' = '#7c4515',
          'scaRNA' = '#e9a3ff',
          'sRNA' = '#e7c08b'
        )
      )
      + ylab('smallRNA proportion')
    )
  ),
      queries=list(
        upset_query(
            intersect=c('diff.75pc', 'undiff.75pc'),
            color='orange',
            fill='orange',
            only_components=c('intersections_matrix', 'Intersection size')
        ),
        upset_query(
            intersect='diff.75pc',
            color='tomato',
            fill='tomato',
            only_components=c('intersections_matrix', 'Intersection size')
        ),
        upset_query(
            intersect='undiff.75pc',
            color='lightseagreen',
            fill='lightseagreen',
            only_components=c('intersections_matrix', 'Intersection size')
        )
    ),
  width_ratio = 0.3,
  set_sizes = (
    upset_set_size(geom = geom_bar(aes(
      fill = gene_biotype, x = group
    ),
    width = 0.8),
    position = 'right') + theme(legend.position = "none") +
      scale_fill_manual(
        values = c(
          'miRNA' = '#8142c8',
          'misc_RNA' = '#f62189',
          'snRNA' = '#008cd4',
          'snoRNA' = '#7c4515',
          'scaRNA' = '#e9a3ff',
          'sRNA' = '#e7c08b'
        )
      )
  ),
  guides = 'over'
)

Intersection plots

quartile

quartile.plot
Figure 7A: Gene expression greater than 75th percentile (intersection)

Figure 7A: Gene expression greater than 75th percentile (intersection)

decile

decile.plot
Figure 7B: Gene expression greater than 90th percentile (intersection)

Figure 7B: Gene expression greater than 90th percentile (intersection)

Volcano plots

volcanoPlots2 <-
  function(res.se,
           string,
           first,
           second,
           color1,
           color2,
           color3,
           ChartTitle) {
    res.se <- res.se[order(res.se$padj), ]
    res.se <-
      rownames_to_column(as.data.frame(res.se[order(res.se$padj), ]))
    names(res.se)[1] <- "Gene"
    res.data <-
      merge(res.se,
            annot,
            by.x = "Gene",
            by.y = "ensembl_gene_id_version")
    res.data <- res.data %>% mutate_all(na_if, "")
    res.data <- res.data %>% mutate_all(na_if, " ")
    res.data <-
      res.data %>% mutate(gene_symbol = coalesce(external_gene_name, Gene))
    fc=1.5
log2fc=log(fc, base = 2)
neg.log2fc = log2fc * -1

    res.data$diffexpressed <- "other.genes"
    res.data$diffexpressed[res.data$log2FoldChange >= log2fc &
                             res.data$padj <= 0.05] <-
      paste("Higher expression in", first)
    res.data$diffexpressed[res.data$log2FoldChange <= neg.log2fc &
                             res.data$padj <= 0.05] <-
      paste("Higher expression in", second)
    res.data$delabel <- ""
    res.data$delabel[res.data$log2FoldChange >= log2fc
                     & res.data$padj <= 0.05
                     &
                       !is.na(res.data$padj)] <-
      res.data$gene_symbol[res.data$log2FoldChange >= log2fc
                           &
                             res.data$padj <= 0.05
                           &
                             !is.na(res.data$padj)]
    res.data$delabel[res.data$log2FoldChange <= neg.log2fc
                     & res.data$padj <= 0.05
                     &
                       !is.na(res.data$padj)] <-
      res.data$gene_symbol[res.data$log2FoldChange <= neg.log2fc
                           &
                             res.data$padj <= 0.05
                           &
                             !is.na(res.data$padj)]
    ggplot(res.data,
             aes(
               x = log2FoldChange,
               y = -log10(padj),
               col = diffexpressed,
               label = delabel
             )) +
      geom_point(alpha = 0.5) +
      xlim(-20, 20) +
      theme_classic() +
      scale_color_manual(name = "Expression", values = c(color1, color2, color3)) +
      # geom_text_repel(
      #   data = subset(res.data, padj <= 0.05),
      #   max.overlaps  = 15,
      #   show.legend = F,
      #   min.segment.length = Inf,
      #   seed = 42,
      #   box.padding = 0.5
      # ) +
      ggtitle(ChartTitle) +
      xlab(paste("log2 fold change")) +
      ylab("-log10 pvalue (adjusted)") +
      theme(legend.text.align = 0)
}
g <- volcanoPlots2(
  res.UndfvsDiff,
  "UndfvsDiff",
  "Undf",
  "Diff",
  "tomato",
  "lightseagreen",
  "grey",
  ChartTitle = "Undifferentiated vs. Differentiated"
)
ggplotly(g)

Figure 6: Volcano plot showing genes overexpressed in undifferentiated and differentiated states. 

#g

Heatmap

Heatmap for the top 30 variable genes:

topVarGenes <- head(order(rowVars(assay(vsd)), decreasing = TRUE), 30)
mat  <- assay(vsd)[ topVarGenes, ]
mat  <- mat - rowMeans(mat)
mat2 <-    merge(mat,
          annot,
          by.x = 'row.names',
          by.y = "ensembl_gene_id_version")
mat2$gene <- paste0(mat2$external_gene_name," (",mat2$gene_biotype,")")
rownames(mat2) <- mat2[,'gene']
mat2 <- mat2[2:9]
heat_colors <- brewer.pal(9, "YlOrRd")
g <- pheatmap(
  mat2,
  color = heat_colors,
  main = "Top 30 variable small RNA genes",
  cluster_rows = T,
  cluster_cols  = T,
  show_rownames = T,
  border_color = NA,
  fontsize = 10,
  scale = "row",
  fontsize_row = 10
)
g
Figure 7: Heat map for top 30 variable small RNA genes

Figure 7: Heat map for top 30 variable small RNA genes

MultiQC report:

MultiQC report is available at this link

Session Information

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] ComplexUpset_1.3.3          PupillometryR_0.0.4        
#>  [3] rlang_1.0.4                 reshape2_1.4.4             
#>  [5] biomaRt_2.52.0              ggrepel_0.9.1              
#>  [7] genefilter_1.78.0           pheatmap_1.0.12            
#>  [9] RColorBrewer_1.1-3          DESeq2_1.36.0              
#> [11] SummarizedExperiment_1.26.1 Biobase_2.56.0             
#> [13] MatrixGenerics_1.8.1        matrixStats_0.62.0         
#> [15] GenomicRanges_1.48.0        GenomeInfoDb_1.32.2        
#> [17] IRanges_2.30.0              S4Vectors_0.34.0           
#> [19] BiocGenerics_0.42.0         plotly_4.10.0              
#> [21] forcats_0.5.1               stringr_1.4.0              
#> [23] dplyr_1.0.9                 purrr_0.3.4                
#> [25] readr_2.1.2                 tidyr_1.2.0                
#> [27] tibble_3.1.8                ggplot2_3.3.6              
#> [29] tidyverse_1.3.2             scales_1.2.0               
#> [31] knitr_1.39                 
#> 
#> loaded via a namespace (and not attached):
#>   [1] googledrive_2.0.0      colorspace_2.0-3       ellipsis_0.3.2        
#>   [4] XVector_0.36.0         fs_1.5.2               rstudioapi_0.13       
#>   [7] farver_2.1.1           bit64_4.0.5            AnnotationDbi_1.58.0  
#>  [10] fansi_1.0.3            lubridate_1.8.0        xml2_1.3.3            
#>  [13] codetools_0.2-18       splines_4.2.1          cachem_1.0.6          
#>  [16] geneplotter_1.74.0     jsonlite_1.8.0         broom_1.0.0           
#>  [19] annotate_1.74.0        dbplyr_2.2.1           png_0.1-7             
#>  [22] compiler_4.2.1         httr_1.4.3             backports_1.4.1       
#>  [25] assertthat_0.2.1       Matrix_1.4-1           fastmap_1.1.0         
#>  [28] lazyeval_0.2.2         gargle_1.2.0           cli_3.3.0             
#>  [31] prettyunits_1.1.1      htmltools_0.5.3        tools_4.2.1           
#>  [34] gtable_0.3.0           glue_1.6.2             GenomeInfoDbData_1.2.8
#>  [37] rappdirs_0.3.3         Rcpp_1.0.9             cellranger_1.1.0      
#>  [40] jquerylib_0.1.4        vctrs_0.4.1            Biostrings_2.64.0     
#>  [43] crosstalk_1.2.0        xfun_0.31              rvest_1.0.2           
#>  [46] lifecycle_1.0.1        XML_3.99-0.10          googlesheets4_1.0.0   
#>  [49] zlibbioc_1.42.0        vroom_1.5.7            hms_1.1.1             
#>  [52] parallel_4.2.1         curl_4.3.2             yaml_2.3.5            
#>  [55] memoise_2.0.1          sass_0.4.2             stringi_1.7.8         
#>  [58] RSQLite_2.2.15         highr_0.9              filelock_1.0.2        
#>  [61] BiocParallel_1.30.3    pkgconfig_2.0.3        bitops_1.0-7          
#>  [64] evaluate_0.15          lattice_0.20-45        patchwork_1.1.1       
#>  [67] htmlwidgets_1.5.4      labeling_0.4.2         bit_4.0.4             
#>  [70] tidyselect_1.1.2       plyr_1.8.7             magrittr_2.0.3        
#>  [73] bookdown_0.27          R6_2.5.1               generics_0.1.3        
#>  [76] DelayedArray_0.22.0    DBI_1.1.3              pillar_1.8.0          
#>  [79] haven_2.5.0            withr_2.5.0            survival_3.3-1        
#>  [82] KEGGREST_1.36.3        RCurl_1.98-1.8         modelr_0.1.8          
#>  [85] crayon_1.5.1           utf8_1.2.2             BiocFileCache_2.4.0   
#>  [88] tzdb_0.3.0             rmarkdown_2.14         progress_1.2.2        
#>  [91] locfit_1.5-9.6         grid_4.2.1             readxl_1.4.0          
#>  [94] data.table_1.14.2      blob_1.2.3             rmdformats_1.0.4      
#>  [97] reprex_2.0.1           digest_0.6.29          xtable_1.8-4          
#> [100] munsell_0.5.0          viridisLite_0.4.0      bslib_0.4.0